Features generation and spotting methods and systems using same

Abstract

An image partitioner is configured to find a partition point that divides a received image into four sub-images each having a pre-selected activated pixel count. A recursion processor is configured to (i) apply the image partitioner to an input image to generate a first partition point and four sub-images and to (ii) recursively apply the image partitioner to at least one of the four sub-images for at least one recursion iteration to generate at least one additional partition point. A formatter is configured to generate a features representation of the input image in a selected format. The features representation is based at least in part on the partition points. The features representation can be used in various ways, such as by a classifier configured to classify the input image based on the features representation.

Description

BACKGROUND[0001]The following relates to the image processing, analysis, classification, comparison, detection, and related arts. The following is described with illustrative reference to spotting applications such as word spotting, logo spotting, signature spotting, and so forth, but will be useful in numerous other applications.[0002]Optical character recognition (OCR) is a known technique for converting an optically scanned handwritten or typed document to an ASCII, XML, or other text-based format. Existing commercial OCR products include, for example, FineReader™ (available from ABBYY USA Software House, Fremont, Calif.). The OCR converted document is readily searched for words of interest. OCR has numerous advantages, but is computationally intensive and sensitive to image quality.[0003]Word spotting (or, more generally, spotting, which can apply to words, objects such as logos, signatures, and so forth, sometimes also referred to as word detection, logo detection or so forth o...

AI Technical Summary

Problems solved by technology

OCR has numerous advantages, but is computationally intensive and sensitive to image quality.

In such an arrangement, any fast rejection stage should produce a low rate of false rejections since any false rejection is not passed onto the downstream stage and hence is irrevocably lost.

However, these features are not strongly discriminatory and tend to produce high false positive rates in the initial classifier.

For example, the aspect ratio feature is highly discriminatory for words of interest that have an unusual aspect ratio, but is less effective for “typical” words that have typical aspect ratios similar to numerous other words.

On the other hand, localized features computed using a sliding window or the like can be strongly discriminatory, but are computationally intensive, and therefore typically not well suited for use in an initial fast rejection stage of a cascaded classifier.

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